Result: Multi-objective genetic algorithm based method for mining optimized fuzzy association rules
Title:
Multi-objective genetic algorithm based method for mining optimized fuzzy association rules
Authors:
Source:
IDEAL 2004 : intelligent data engineering and automated learning (Exeter, 25-27 August 2004)Lecture notes in computer science. :758-764
Publisher Information:
Berlin: Springer, 2004.
Publication Year:
2004
Physical Description:
print, 8 ref
Original Material:
INIST-CNRS
Subject Terms:
Biochemistry, molecular biology, biophysics, Biochimie, biologie moléculaire, biophysique, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Algorithme génétique, Genetic algorithm, Algoritmo genético, Analyse donnée, Data analysis, Análisis datos, Association statistique, Statistical association, Asociación estadística, Confiance, Confidence, Confianza, Ensemble flou, Fuzzy set, Conjunto difuso, Fouille donnée, Data mining, Busca dato, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Logique floue, Fuzzy logic, Lógica difusa, Programmation multiobjectif, Multiobjective programming, Programación multiobjetivo, Règle association, Association rule, Regla asociación
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Engineering, Firat University, 23119 Elazig, Turkey
ADSA Lab & Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
ADSA Lab & Department of Computer Science, University of Calgary, Calgary, Alberta, Canada
ISSN:
0302-9743
Rights:
Copyright 2004 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.16176675
Database:
PASCAL Archive
Further Information
This paper introduces optimized fuzzy association rules mining. We propose a multi-objective Genetic Algorithm (GA) based approach for mining fuzzy association rules containing instantiated and uninstantiated attributes. According to our method, fuzzy association rules can contain an arbitrary number of uninstantiated attributes. The method uses three bjectives for the rule mining process: support, confidence and number of fuzzy sets. Experimental results conducted on a real data set demonstrate the effectiveness and applicability of the proposed approach.